Method for developing data warehouse logical data models using shared subject areas

ABSTRACT

A logical data model (LDM) design methodology that utilizes common or shared subject areas, developed for applications across two or more industries, in the design and construction of data warehouse solutions and logical data models for specific customers. The development and use of shared subject areas provides for more effective new LDM development through re-use of common elements and quicker deployment of horizontal applications on all industries.

FIELD OF THE INVENTION

The present invention relates generally to Data Warehouse solutions, andmore particularly, to the design and construction of logical data modelsthat define the data elements can be stored in the data warehouse andhow they relate to one another.

BACKGROUND OF THE INVENTION

NCR Corporation has developed, and continues to develop and improve,data warehouse solutions and applications for numerous industries suchas the Communications, Travel and Transportation, Retail, E-Business,Financial Services and Insurance, and Manufacturing industries.

The Enterprise Data Warehouse (EDW) has proved a strategic weapon formost modern organizations. It should be active, dynamic and flexible inorder to cope with changing business requirements. It should provide astrategic background to support changing business relationships.

The foundation of the enterprise data warehouse is a comprehensive andresponsive logical data model addressing challenges in the near futurewithout compromising existing business processes. A logical data modelis a graphical representation of the way data is organized in a datawarehouse environment. The logical data model specifically defines whichindividual data elements can be stored and how they relate to oneanother to provide a model of the business information. The data modelultimately defines which business questions can be answered from thedata warehouse and thus determines the business value of the entiredecision support system.

A properly designed LDM provides a foundation for more effective sales,marketing, and customer management; and supports customer relationshipmanagement (CRM) requirements related to identifying, acquiring,retaining and growing valuable customers. A logical data model reflectsthe operating principles and policies of a business and provides theunderlying structure for the data imported into the data warehouse, inthe following ways:

-   -   It serves as a road map for achieving data integration in an        organization. It is a guide for development over the long term.    -   It provides a neutral cross-functional view, not Accounting's        view or Marketing's view.    -   It shows interlocking parts. Expanding the model for future        enhancements is a lot easier if you understand all the        interdependent parts.    -   It is a foundation upon which to build applications or business        views.    -   It is a tool that allows an organization to recognize and        control data redundancy. Data redundancy can lead to inaccurate        and inconsistent reporting of business information.    -   It allows an organization to see relationships between data        elements.    -   It is the starting point for developing a physical database        design.    -   It aids the communication between an analyst and the business        user.    -   It is a rigorous technique that imposes discipline on the        warehouse development process and leads to the development of        stable, robust, long term and reliable solutions.    -   A model is a communication tool—it allows an organization to        understand their data warehouse, prior to, during and after        implementation.

Different industries have different information requirements, datasources, data uses and accordingly, data warehouse requirements. Eachindustry data warehouse solution is constructed in accordance with adifferent logical data model. Even within the same industry, differentcustomer requirements will result in different logical data modelstructures.

As stated earlier, NCR Corporation has developed data warehousesolutions and applications for numerous industries. Logical data modelsfor several of these data warehouse solutions are described in thefollowing patent applications:

U.S. patent application Ser. No. 09/838,101, filed on Feb. 14, 2001,describes a logical data model for the Communications industry. Theapplication, titled “LOGICAL DATA MODEL FOR COMMUNICATIONS INDUSTRYCUSTOMER RELATIONSHIP MANAGEMENT,” is incorporated herein by reference.

U.S. patent application Ser. No. 09/921,566, filed on Aug. 6, 2001,describes a logical data model for the Airline industry. Theapplication, titled “COMPUTER IMPLEMENTED CUSTOMER VALUE MODEL INAIRLINE INDUSTRY,” is incorporated herein by reference.

U.S. patent application Ser. No. 09/990,539, filed on Nov. 16, 2001,describes a logical data model for the E-Business industry. Theapplication, titled “SYSTEM AND METHOD FOR CAPTURING AND STORINGINFORMATION CONCERNING WEB VISITOR BROWSING ACTIVITIES IN A DATAWAREHOUSE,” is incorporated herein by reference.

U.S. patent application Ser. No. 10/017,146, filed on Dec. 14, 2001,describes a logical data model for the Retail industry. The application,titled “SYSTEM AND METHOD FOR CAPTURING AND STORING INFORMATIONCONCERNING RETAIL STORE OPERATIONS,” is incorporated herein byreference.

U.S. patent application Ser. No. 10/027,967, filed on Dec. 21, 2001,describes a logical data model for the Travel and Transportationindustry. The application, titled “SYSTEM AND METHOD FOR CAPTURING ANDSTORING BUSINESS INFORMATION FOR THE TRAVEL AND TRANSPORTIONINDUSTRIES,” is incorporated herein by reference.

U.S. patent application Ser. No. 10/190,099, filed on Jul. 3, 2002,describes a logical data model for the Financial industry. Theapplication, titled “SYSTEM AND METHOD FOR CAPTURING AND STORINGFINANCIAL MANAGEMENT INFORMATION,” is incorporated herein by reference.

As apparent from a review of the above referenced logical data models,the development and modification of logical data models requiresextensive business knowledge, customer collaboration, and use ofdevelopment resources. A method for simplifying and improving logicaldata model development and reducing the amount of time and resourcesrequired in the development process is desired.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a new and usefulsystem and method for capturing, storing and organizing informationwithin a data warehouse.

It is a further object of the present invention to provide a new anduseful method for constructing logical data models.

The foregoing objects are accomplished through implementation of alogical data model design methodology that utilizes common or sharedsubject areas, developed for applications across two or more industries,in the design and construction of data warehouse solutions and logicaldata models for specific customers. The development and use of sharedsubject areas provides for more effective new LDM development throughre-use of common elements and quicker deployment of horizontalapplications on all industries.

The method described herein includes the steps of developing at leastone shared subject area, said shared subject area comprising a pluralityof entities and relationships defining the manner in which basicinformation common to two or more industries is stored within adatabase; and including said shared subject area within said logicaldata model for a data warehouse customer from one of the two or moreindustries.

Still other objects and advantages of the present invention will becomereadily apparent to those skilled in the art from the following detaileddescription, wherein the preferred embodiments of the invention areshown and described, simply by way of illustration of the best modecontemplated of carrying out the invention. As will be realized, theinvention is capable of other and different embodiments, and its severaldetails are capable of modifications in various obvious respects, allwithout departing from the invention. Accordingly, the drawings anddescription thereof are to be regarded as illustrative in nature, andnot as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not bylimitation, in the Figures of the accompanying drawings, whereinelements having the same reference numeral designations represent likeelements throughout and wherein:

FIG. 1 is a high level functional diagram of an enterprise datawarehouse decision support system incorporating a logical data model.

FIG. 2 provides an overview of the hardware components of an enterprisedata warehouse solution.

FIG. 3 provides an overview of the software components of an enterprisedata warehouse solution.

FIG. 4 is a diagram of a data model hierarchy.

FIG. 5 is a subject area model of a communications industry logical datamodel.

FIG. 6 is a subject area model of a logical data model for theE-Business industries, illustrating the subject areas included withinthe LDM.

FIG. 7 provides an illustration of a shared subject area architecture inaccordance with the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION Backbone for Decision SupportSystems

FIG. 1 is a high level diagram of how a logical data model 102 relatesto operational databases 104 and business intelligence applications 106,e.g., decision support systems. Decision support systems provide userswith the ability to quickly analyze large amounts of customer data. Forexample, users use decision support tools to perform trend analysis onsales and financial information or to drill down into masses of salesstatistics to isolate the most volatile products.

The logical data model 102 provides the backbone for the decisionsupport components of Customer Relationship Management. The logical datamodel 102 provides the capability to support huge amounts of detaileddata in the data warehouse, analyze it using standard, ad hoc, andforecasting queries, and answer the following types of businessquestions: What happened? Why did it happen? What will happen?

As shown in FIG. 1, customer data 108 is the central focus of thecustomer relationship management system. Customer data 108 is accessedby business intelligence applications 106 via a standard data storageand access component 110 and organized according to logical data model102. On-line transaction processing (OLTP) 112 interacts withoperational databases 104 containing data which is mapped to thecustomer data 108.

OLTP 112 is processing transactions as the transactions are received bythe computer. Also called “online” or “real-time” systems, transactionprocessing means that master files are updated as soon as transactionsare entered at terminals or received over communications lines. It alsoimplies that confirmations are returned to the sender.

Organizations increasingly rely on computers to keep everythingup-to-date all the time. A manager might need to know how many items areleft on the shelf, what the latest price of a stock is or what the valueof a financial portfolio is at any given moment.

This decision support system shown in FIG. 1 is designed to contain verylarge volumes of detail data that are maintained for long periods oftime. This is particularly important to communications service providersfor developing experience and profitability on customer segments, membersegments, product lines, and providers. The detail data is intended tocapture the complete record of customer transactions. Detailedtransactions are sourced from operational systems and contain allrelevant elements in the customer transaction.

The communications data environment is complex and extensive. Thelogical data model's focus is on customers, contracts, products,transactions, market channels, and marketing campaigns. Although thelogical data model does not encompass the general administrative orinstitutional investment areas of the enterprise, it can be extended tothese areas and others without compromise.

Hardware Overview

FIG. 2 provides an overview of the hardware required for a datawarehouse solution. The basic components consist of an NCR CorporationTeradata Scalable Data Warehouse 201, and administrative server 203, andclient and administrative workstations 205 and 207, respectively. Thecomponents communicate with each other through a Local Area Network(LAN) or Wide Area Network (WAN), identified by reference numeral 209.

The system shown in FIG. 2 may support a communications providercustomer-centric warehouse 201 as defined by the Communications LogicalData Model, described below. The application server 203 supportscustomer relationship management applications, such as NCR Corporation'sCustomer Business Intelligence (CustomerBI), SpotLIGHT, NCR CRM andFraudSENTRY applications. The solution requires a client workstation 205for the solution administrator and workstation 207 for the marketinganalyst. If only one workstation is available, separate access paths maybe configured to the applications for the solution administrator and tothe applications for the marketing analyst.

Software Overview

FIG. 3 provides an overview of the software components that make up anexemplary knowledge management solution for a communications provider.The Communications logical data model defines the structure of thecommunications provider customer-centric database residing on TeradataSDW. The software components illustrated include the following datawarehouse components 301: an NCR Teradata Relational Database ManagementSystem (RDMS) operating on a UNIX or Microsoft Windows NT operatingsystem; solution warehouse utilities, statistical analysis applications,data mining applications, and retention scripts.

Application/Network File Server components 303 include: MicrosoftCorporation Windows NT operating system, Cognos Incorporated Impromptu®report and query generation tool, Cognos Incorporated PowerPlay® datamining and report generation tool, internet information server software,Teradata ODBC Drivers, and NCR Corporation CustomerBI application.

Marketing Analyst Web-Based Workstation components 305 include:Microsoft Corporation Windows 95, 98, NT, or Win 2000 operating system,and an internet browser application.

Marketing Analyst LAN-Based Workstation components 307 include: CognosIncorporated Power Play User Version, Cognos Incorporated Impromptu UserVersion, Teradata ODBC drivers, NCR Corporation CustomerBI application,NCR Corporation CRM for Communications application, and MicrosoftCorporation Windows 95, 98, NT, or Win 2000 operating system software.

Communications Management LAN-Based Workstation 309 components include:Microsoft Corporations Windows 95, 98, NT, or Win 2000 operating system,Teradata ODBC Drivers, NCR Corporation CRM for Communicationsapplication, and Communications Performance Dashboard.

Fraud Management Analyst LAN-Based Workstation 311 components include:Cognos Incorporated Power Play User Version, Cognos IncorporatedImpromptuUser Version, Teradata ODBC drivers, and Microsoft CorporationWindows 95, 98, NT, or Win 2000 operating system software.

Solution Administrator Workstation 313 software components include:Cognos Incorporated Power Play Administrator Version, CognosIncorporated Impromptu Administrator Version, Teradata ODBC drivers, NCRCorporation CustomerBI appication, and Microsoft Corporation Windows 95,98, NT, or Win 2000 operating system software. Additional applicationsmay include Platinum Technologies, Inc. ERWIN database modelingapplication, NCR Corporation WINDDI and Queryman applications, CBIAutomation Tool, and MKS Toolkit software.

Data Sources

In the logical data model, the communications services provider'sinternal databases provide most of the data loaded into the warehouse.Generally the internal databases can be divided into the followingcategories:

-   -   Account data including information associated with the financial        business transactions and financial records related to the        preparation of statements concerning the operating results of        the business. Likely source: Billing System.    -   Advertising data including information regarding        company-sponsored campaign and product promotions. Likely        source: Marketing systems.    -   Contract data including information associated with the        agreement between a CSP and its customers. Likely source: Order        Entry systems.    -   Offering data including information about the products and        services being offered, the geographical area in which they are        offered, etc. Likely source: Marketing systems.    -   Party data including information about any person, business,        group or association of interest or involved with the        communications services provider. Likely source: Marketing        Systems or an external data provider (as described below).    -   Revenue data including the billing information associated with a        customer's use or subscription to a product offering. Likely        source: Billing Systems.

The logical data model may also incorporate data imported from sourcesexternal to the communications services provider, including, forexample, the following:

-   -   Firmographic and business credit data, which is detailed data        about the business customer. This information may be obtained        from Dun & Bradstreet (www.dnb.com).    -   Census data, which includes detailed statistical information        about the population and economy of geographical areas. Census        data can usually obtained as a report from Acxiom Corporation        (www.axciom.com).    -   Psychographic data, which includes detailed information about        residential households. This information may also be obtained as        part of the census report from Acxiom Corporation.    -   Contact lists, which are often available form the Polk Company        (www.polk.com).

Logical Data Model Design Basics

As stated earlier, a properly designed logical data model provides afoundation for more effective sales, marketing, and operationsmanagement and supports the customer relationship managementrequirements related to identifying, acquiring, retaining and growingvaluable customers.

A logical data model (LDM) is an abstract construct that is physicallyrealized in the database or data warehouse. The data model provides anarchitecture for the information that will be included in a datawarehouse. The database provides the physical realization of thatarchitecture in a form that can be efficiently maintained and used.There may well be some differences between the logical data model andthe final database design. The database may include some tables (summarytables, etc.) or columns that have no direct correlation in the logicaldata model. Elements in the logical model may be grouped differently inthe physical database.

A logical data model is organized by Subject Areas, each comprised ofnumerous Entities, Attributes and Relationships, as illustrated in FIG.4. The data model hierarchy includes one or more Subject Areas 403. TheSubject Areas 401 include one or more Entities 405 each havingattributes 407 and relationships 409. Relationships 409 between two ormore Entities 405 are further defined by Cardinality 411. TheRelationships 409 define which entities are connected to other entitiesand the cardinality of the relationships. The Attributes 407 describe afact about the Entity 405. Each of these elements will be described ingreater detail below.

Subject Area

A subject area is a subset of objects taken from the universe of dataobjects for a particular line of business or industry that focus on aparticular Business Process. Typically, a subject area is created tohelp manage large data architectures that may encompass multiplebusiness processes or business subjects. This is the highest-level dataconcept within a conceptual entity/relationship (E/R) model. Workingwith subject areas is especially useful when designing and maintaining alarge or complex data model. Dividing the enterprise into severaldistinct subject areas allows different groups within an organization toconcentrate on the processes and functions pertinent to their businessarea.

Entity

An Entity represents a person, place, thing, concept, or event (e.g.PARTY, ACCOUNT, INVOICE, etc.). It represents something for which thebusiness has the means and the will to collect and store data. An Entitymust have distinguishable occurrences, e.g., one must be able touniquely identify each occurrence of an entity with a primary key (e.g.Party Identifier, Account Identifier, Invoice Number, etc.). An Entityis typically named with a unique singular noun or noun phrase (e.g.,PARTY, BILLING STATEMENT, etc.) that describes one occurrence of theEntity and cannot be used for any other Entity. It should be exclusiveof every other Entity in the database. An Entity cannot appear more thanonce in the conceptual entity/relationship (E/R) model. Each Entity mayhave relationships to other Entities residing in its own Subject Area orin other Subject Areas.

Attribute

An Attribute is a data fact about an Entity or Relationship. It is alogical (not physical) construct. It is data in its atomic form. Inother words, it is the lowest level of information that still hasbusiness meaning without further decomposition. An example would beFIRST NAME, or LAST NAME. An example of an invalid attribute would bePERSON NAME if it includes both the first and last names, as this couldbe further decomposed into the separate, definable (first name, lastname) data facts.

Relationship

A Relationship is an association that links occurrences of one or moreEntities. A Relationship must connect at least one Entity. If only oneEntity is connected, the Relationship is said to be Recursive. ARelationship is described by a noun or passive verb or verb phase thatdescribes the action taken in the Relationship. A Relationship representa static state of being between the occurrences of the Entities itconnects. Relationships are not intended to represent processes or dataflows. They cannot be linked to another Relationships. They mayoptionally represent future, present, and/or past relatedness. The timeframe must be explicitly defined in the data definition. Relationshipsmay contain attributes. In a normalized model, a Relationship containingAttributes will result in the creation of an Entity.

Cardinality

In order for a data model to be considered accurate, it must containboth the maximum and minimum number of Entity occurrences expected. Thisis controlled by rules of cardinality, which describes a relationshipbetween two Entities based on how many occurrences of one Entity typemay exist relative to the occurrence of the other Entity. Typically, itis a ratio, commonly depicted as a one-to-one (1:1); one-to-many (1:N);and many-to-many (M:N) relationship.

The maximum cardinality may be an infinite number or a fixed number butnever zero. The minimum cardinality may be zero, or some other positivenumber, but it must be less than or equal to the maximum cardinality forthe same relationship.

Illustrative logical data models for the Communications Industry andE-Business Industry will now be described.

EXAMPLE 1 Communications Industry Logical Data Model

FIG. 5 is a subject area model of the Communications Industry logicaldata model, illustrating the subject areas included within the LDM. TheSubject Area Model is a one page over-view that defines, at a highlevel, the scope and data requirements of the solution. Each facetwithin the logical data model will be illustrated in the Figures anddescribed in greater detail below.

The subject area model shown in FIG. 5 depicts the major relationshipsbetween subject areas. The boxes represent the subject areas. Each linerepresents a relationship between subject areas. The solid circle at theend of a relationship line represents the target of a ‘many’relationship. For example, the relationship between LOCATION and PARTYis many-to-many. A LOCATION can be related to one or many PARTIES and aPARTY can have zero, one or many LOCATIONS. Only the major relationshipsare shown in this diagram.

The major subject areas are defined below:

Advertisement

The Advertisement subject area 501 contains information about thestrategic sales and information gathering initiatives that are used bythe Communications Service Provider to support their businessobjectives. Information can be captured that will enable theCommunications Service Provider to create, manage and measureinformation on campaigns and responses by individual to track campaigneffectiveness over time. Information about Surveys, the Scripts used bya Survey, the logistics and conditions that existed when the Survey wasconducted, and the results of the Survey can also be captured. Detailedinformation about the Contact Lists that are utilized by a Campaign or aSurvey is also included.

Event

The Event subject area 503 contains information regarding theoccurrences of significant incidents to provide business users with acomplete view of the customer lifecycle across different stages of theirrelationship with the company. Events may be used by the CommunicationsService Provider for such purposes as tracking Customer Service,initiating special Promotions and Campaigns, and targeting Customers orother Parties and Accounts to a particular Campaign because they whereaffected by an Event.

Detailed information about an Event, such as the reasons for the Event,how the Event was resolved, the category of the Event, all involvedParties, and the relationships that can exist between Events, can alsobe captured. There are many types of Events that may occur, for exampleEvent may include a Contact, a Service Order, a Billing StatementAdjustment, a Fraud event based on subscription or call usage, or even ageographic occurrence such as a power outage, service disruption, orregional holiday.

The Event entities enable Marketing and Sales to determine and track keyfactors related to churn and growth. Event and status changes cantrigger marketing actions such as win-back, cross-sell or up-sellcampaigns. For example, contract expiration within 60 days may trigger arenewal notice and subsequent direct mail or calls to retain thecustomer.

Finance

The Finance subject area 505 contains Account, Billing, Payment, OtherRevenue and Cost Information. Accounts are relationships that existbetween the Communications Service Provider and a Customer at the billinvoice level. Historical billing information is represented via theInvoices that are generated by the Communications Service Provider toAccounts for goods and services rendered. A history of the Payments madeto satisfy the Invoices is also captured. Costs can be one-time chargesthat are incurred on a specific date or they may be charged over aperiod of time. There are many types of Costs that can be captured, andeach type of cost is classified by a Cost Item Category.

“How much and how long” are the basis for the Finance part of the model.The model takes key aspects of the call information and providesrelationships to give new insight into Revenue and Usage patterns.Affinities between the usage and revenue, products and customers becomevisible, extending the understanding of the “how and why” of customeruse. Combined with the other components of the model, the billing andusage detail allows marketers to find the most profitable productbundles and customized services for infinite segments or individuals.

Location

The Location subject area 507 provides the Addresses and geographicalareas that are important to the Communications Service Provider.Addresses and geographical areas are used to locate customers andequipment, provide customer listings, target marketing campaigns,specify product availability's, and support artificially createdboundaries such as Sales Districts and Test Markets for sales andmanagement purposes. The Location subject area also provides for thecapturing of historical demographic data about a geographical area.

Network

The Network subject area 509 provides data on how the customer accessesthe products and services offered by the Communications Service Providerand provides rudimentary information about the physical equipment thatis used to provide those services. Detailed data also exists, at aMediated and Rated Call level for both wireline and wireless, about howand when a Customer utilizes a product or service. The Network subjectarea also includes placeholders for Fraud Profiling.

Offer

The Offer subject area 511 provides information about the Products orProduct Packages (Offerings) that are sold by the Communications ServiceProvider and consumed by their customers.

Information is captured about the terms and conditions associated withan Offering, the Contracts that cover an Offering, the Promotions thatare used as incentives to buy an Offering, and a history of thoseOfferings to which a Customer subscribes.

The Offer subject area enables any depth or number of producthierarchies. This allows marketers to provide unlimited product andservice bundling to meet any customer segments' needs. The Offer subjectarea splits arcane product structures down to their simplest components(product hierarchy, product, capability, and rates) for easily answeringproduct performance or product projection (what if) questions.

Party

The Party subject area 513 contains information about any person,business, group, or association that is of business interest or isinvolved with the Communications Service Provider. Each Party will haveone or more roles, which will designate why that Party is of businessinterest to the Communications Service Provider. A history of theseroles will be recorded for each role that a Party fulfills. For example,a customer is a Party that buys or uses the company's products orservices and actually or potentially generates revenue. This same Partymay also be a vendor who sells goods and services to the CommunicationsService Provider.

Extensive historical information about a Party, such as the AwardsPrograms that they participate in, Demographic Profiling includingunlimited types of demographic, psychographic, and firmagraphic data andSegmentation including scores such as propensity to chum, propensity tobuy, or customer worth can also be captured.

Additional details concerning the communications industry logical datamodel described above are provided in U.S. patent application Ser. No.09/838,101, titled “LOGICAL DATA MODEL FOR COMMUNICATIONS INDUSTRYCUSTOMER RELATIONSHIP MANAGEMENT,” by M. Lundhoj et al. and filed onApr. 19, 2001.

EXAMPLE 2 E-Business Logical Data Model

FIG. 6 is a subject area model of a logical data model for theE-Business industries, illustrating the subject areas included withinthe LDM. The Subject Area Model is a one-page overview that defines, ata high level, the scope and data requirements of the solution.

The subject area model shown in FIG. 6 illustrates some of the majorrelationships between subject areas. The boxes represent the subjectareas. Each line represents a relationship between subject areas. Thesolid circle at the end of a relationship line represents the target ofa ‘many’ relationship. An open circle at the end of a relationship lineindicates that the relationship is optional.

The E-Business LDM is organized into fifteen major subject areas titled:ADVERTISEMENT, CONTACT INFORMATION, ISP, ITEM, LOCATION, MULTIMEDIACOMPONENT, PARTY, PRIVACY, PROFILE, PROMOTION, TRANSACTION ACTIVITY,VENDOR, VISIT, WEB SERVER, WEB SITE. A brief description of each subjectarea follows:

Advertisement

The Advertisement Subject Area 601 determines the effectiveness of adcampaigns by collecting information on the cost of ads by type, andcomparing this information with the number of times an exposure to thead delivered a customer to a particular item or site.

Contact Information

The Contact Information subject area 605 stores contact information forcustomers & organizations, including mailing addresses, email addresses,and telephone numbers.

ISP

The ISP subject area 607 contains information covering all aspects ofInternet Service Provider activity.

Item

The Item subject area 609 stores information concerning each piece ofmerchandise or each service provided by the E-business retailer.Included would be a description, how the item was classified, price,cost, the number in inventory, etc.

Location

The Location subject area 611 stores information on all physical andvirtual sites owned or leased by the retailer to support the sale ofgoods, distribution, and storage. Would include kiosks, warehouses,offices, as well as internet sites.

Multimedia Component

The Multimedia Component subject area 613 stores multimedia elementsthat can be use to construct a web page, such as ads, catalogues, etc.

Party

The Party subject area 629 captures information about the users involvedin web transactions and/or interactions. This area maintains informationabout customer's payment accounts, and household and organizationalaffiliations, and it maps customers to entries in the Profile SubjectArea.

Privacy

The Privacy subject area 615 stores information about privacypermissions from individuals, households and organizations of interestto the enterprise.

Profile

The Profile subject area 617 stores information concerning customersegments of interest to the enterprise. This information is typicallypurchased from a third party.

Promotion

The Promotion subject area 619 contains information concerningpromotions, which are defined as marketing efforts, which are differentfrom normal practice and designed for a specific purpose. Information isstored on the various components of the promotion, including the itemsand ads included, the type of ad, and the market segments targeted.

Transaction Activity

The Transaction Activity subject area 623 stores information concerninga customer's interaction with the company involving the sale or returnof an item and the price and discounts associated with that item. Itmaps customers to entries in the Address Area, the item(s) of interest,and the associate dealing with the customer.

Vendor

The Vendor subject area 625 stores information about parties from whichthe company purchases goods and services. This would include informationconcerning purchase orders, returns, and items shipped directly to thecompany or drop shipped to a customer.

Visit

The Visit subject area 627 stores information concerning a customer'shistory at a virtual store's web site. Included would be informationabout the ads that triggered the visit, the web pages browsed, and theitems of interest to the consumer.

Web Server

The Web Server subject area 631 contains summary information,operational metrics and errors about the physical server devicesservicing a given web visit by a customer.

Web Site

The Web Site Subject area 633 stores information about the company's websites including page components, page generation, and web page type.

Additional details concerning the e-business industry logical data modeldescribed above are provided in U.S. patent application Ser. No.09/990,539, titled “SYSTEM AND METHOD FOR CAPTURING AND STORINGINFORMATION CONCERNING WEB VISITOR BROWSING ACTIVITIES IN A DATAWAREHOUSE,” by Scott D. Carty et al. and filed on Nov. 16, 2001.

Logical Data Model Sharable Subject Areas

As can be seen in the two logical data model examples described aboveand illustrated in FIGS. 5 and 6, some of the subject areas in the twomodels are similarly named and described, including much of the sameinformation. Corresponding subject areas included in the Communicationsand E-Business logical data models including similar information areADVERTISEMENT subject areas 501 and 601, LOCATION subject areas 507 and611, and PARTY subject areas 513 and 529.

As the number of industry Logical Data Models being sold and installedat customers' sites proliferates, the industry LDMs will become evenmore important than they are today—being the foundation that supportsall data warehouse solutions. To simplify and improve LDM and datawarehouse solution development, there is thus now a greater need thanever before to implement a common naming and data modeling standardacross all industry LDMs, and instigate the utilization of sharablesubject areas across industry LDMs

An architecture for a logical data model utilizing sharable subjectareas may include three conceptual layers:

1. The LOWEST level is maintained only once and may include subclassesfor different industries. The lowest level would have subject areascontaining the basic information that are common across two or moreindustries. For example, Location, Party, Geography, Customer, E-Biz,etc. These sharable subject areas will be integrated into each industryLDM and delivered as one integrated LDM.

2. The MIDDLE level would be industry specific and may includesub-classing within industries, for example wire-line vs. wireless forthe Communications industry. Each industry would have industry specificentities and attributes add-ons to the common subject areas.

Each industry may also have INDUSTRY “extensions” to each of the commonsubject areas. For example, the Retail LDM may have an INDUSTRYextension to Geography, e.g. Trading Zone.

3. The TOP level would be Customer Specific. This level is the customerlevel, and contains their implementation specific enhancements that maybe proprietary to the customer.

FIG. 7 provides an illustration of a shared subject area architecture,wherein component subject areas containing basic information common totwo or more industries are utilized in the design and construction oflogical data models for multiple industries and customers. Logical datamodels are illustrated for several industries including Communications701, Retail 703, Manufacturing 705, Financial Services and Insurance707, E-Business 709, and Travel 711. A portion of each one of theseindustry logical data models is constructed through use of one or moreof the shared subject areas contained within the group 713. Examples ofa few of the shared subject areas that can be constructed for use withintwo or more industry models include:

an advertisement subject area defining the manner in which informationabout sales, promotions and advertising is stored within a database;

a clickstream subject area 717 defining the manner in which informationconcerning web visitors and visitor web activity is stored within adatabase;

a financial management subject 721 area defining the manner in whichfinancial information of interest to two or more industries is storedwithin a database;

a location subject area 719 defining the manner in which informationconcerning physical and virtual properties is stored within a database;

a party subject area 715 defining the manner in which information aboutany person, business, group, or association that is of business interestis stored within a database; and

a privacy subject area defining the manner in which information aboutprivacy permissions from individuals, households and organizations isstored within a database.

CONCLUSION

The Figures and description of the invention provided above reveal a newand useful method for constructing logical data models. Althoughexamples of logical data models for the Communications and E-Businessindustries have been illustrated and described, the invention is notlimited to use within those two industries. Similarly, although examplesof shared subject areas for Party information, Clickstream information,Location information, Financial Management information and Advertisementinformation have been described, many other shared subject areas arepossible within the scope of the present invention.

A logical data model design methodology utilizing shared subject areasprovides for more effective new LDM development through re-use of commonelements and quicker deployment of horizontal applications on allindustries. Shared subject areas enable Professional Services (PS)consultants who work with multiple LDMs to leverage knowledge acrossLDMs and facilitate customer/PS combining of LDMs.

Shared subject areas represent “configurable” LDM components thatfacilitate the development of hybrid business models. For example, acommunications company that has retail store outlets to sell wirelessservices crosses the retail and communications LDMs. A component basedarchitecture allows easy configuration of LDMs for more complex businessmodels.

The foregoing description of the preferred embodiment of the inventionhas been presented for the purposes of illustration and description. Itis not intended to be exhaustive or to limit the invention to theprecise form disclosed. Many modifications and variations are possiblein light of the above teaching. It is intended that the scope of theinvention be limited not by this detailed description, but rather by theclaims appended hereto.

1. A method for constructing a data warehouse for a customer within aspecific industry, said method comprising the steps of: selecting atleast one shared subject area from a plurality of predefined sharedsubject areas for inclusion in a logical data model for said datawarehouse, each one of said predefined shared subject areas comprising aplurality of entities and relationships defining the manner in whichbasic information common to two or more industries is stored within adatabase; and populating said data warehouse with data in accordancewith said logical data model.
 2. The method for constructing a datawarehouse for a customer within a specific industry in accordance withclaim 1, said method further comprising the step of: including industryspecific entities and attributes add-ons to said selected shared subjectarea within said logical data model for said data warehouse.
 3. Themethod for constructing a data warehouse for a customer within aspecific industry in accordance with claim 1, said method furthercomprising the step of: including an industry specific extension to saidselected shared subject area within said logical data model for saiddata warehouse.
 4. The method for constructing a data warehouse for acustomer within a specific industry in accordance with claim 1, whereinsaid specific industry comprises one member of the group of industriescomprising: communications; retail; manufacturing; financial services;insurance; e-business; travel; and transportation.
 5. The method forconstructing a data warehouse for a customer within a specific industryin accordance with claim 1, wherein said at least one shared subjectarea comprises at least one member of the group of predefined subjectareas comprising: an advertisement subject area defining the manner inwhich information about sales, promotions and advertising of interest totwo or more industries is stored within a database; a clickstreamsubject area defining the manner in which information concerning webvisitors and visitor web activity of interest to two or more industriesis stored within a database; a financial management subject areadefining the manner in which financial information of interest to two ormore industries is stored within a database; a location subject areadefining the manner in which information concerning physical and virtualproperties of interest to two or more industries is stored within adatabase; a party subject area defining the manner in which informationabout any person, business, group, or association that is of businessinterest to two or more industries is stored within a database; and aprivacy subject area defining the manner in which information aboutprivacy permissions from individuals, households and organizations ofinterest to two or more industries is stored within a database.
 6. Adata warehouse system for a customer within a specific industry,comprising: a processor a relational database for holding information,said information being organized within said relational database inaccordance with a logical data model; said logical data model includinga plurality of subject areas, each one of said subject areas includingentities and relationships defining the manner in which subsets of saidinformation is stored and organized within said data warehouse; and saidplurality of subject areas including at least one shared subject areaselected from a plurality of predefined shared subject areas, each oneof said predefined shared subject area comprising a plurality ofentities and relationships defining the manner in which basicinformation common to two or more industries is stored within adatabase.
 7. The data warehouse system for a customer within a specificindustry in accordance with claim 6, wherein: said logical data modelfurther includes industry specific entities and attributes add-onsincluded in said at least one shared subject area.
 8. The data warehousefor a customer within a specific industry in accordance with claim 6,wherein: said logical data model further includes an industry specificextension appended to said at least one shared subject area.
 9. The datawarehouse for a customer within a specific industry in accordance withclaim 6, wherein said specific industry comprises one member of thegroup of industries comprising: communications; retail; manufacturing;financial services; insurance; e-business; travel; and transportation.10. The data warehouse for a customer within a specific industry inaccordance with claim 6, wherein said at least one shared subject areacomprises at least one member of the group of predefined subject areascomprising: an advertisement subject area defining the manner in whichinformation about sales, promotions and advertising of interest to twoor more industries is stored within a database; a clickstream subjectarea defining the manner in which information concerning web visitorsand visitor web activity of interest to two or more industries is storedwithin a database; a financial management subject area defining themanner in which financial information of interest to two or moreindustries is stored within a database; a location subject area definingthe manner in which information concerning physical and virtualproperties of interest to two or more industries is stored within adatabase; a party subject area defining the manner in which informationabout any person, business, group, or association that is of businessinterest to two or more industries is stored within a database; and aprivacy subject area defining the manner in which information aboutprivacy permissions from individuals, households and organizations ofinterest to two or more industries is stored within a database.